Spectral Color Prediction based on Neugebauer Primary Colors and Improved PSO-DNN Model

Color accuracy in conventional and digital printing processes relies on press characterization to establish the relationship between input device values and output colorimetric or spectral reflectance values. Conventional models, such as Murray–Davies, Clapper–Yule, Yule–Nielsen, and Yule–Nielsen modified spectral Neugebauer, are renowned for providing accurate chromatic and spectral predictions. However, they fall short of accounting for the effects of black ink use and struggle to predict light hues accurately. In order to predict more accurately the color fingerprint, spectral reflectance, of halftone printed images, this study introduces a novel machine-learning-based deep neural network combined with the improved particle swarm optimization algorithm. This enables implementing a spectral reflectance color prediction model for CMYK printing, which eliminates the need to adjust for dot gain during printing. By evaluating this model on a lithographic offset press, we demonstrate its superior performance evidenced by significantly lower root mean square error and color difference (ΔE ∗ 00) values compared to existing methods. This approach minimizes color deviations during printing and reduces material and energy consumption, thereby ultimately enhancing the quality of printed materials.